Statistical Optimization by the Response Surface Methodology of Direct Recycled Aluminum-Alumina Metal Matrix Composite (MMC-AlR) Employing the Metal Forming Process

Abstract

<jats:p>In this study, the response surface methodology (RSM) and desirability function (DF) were utilized to optimize the recycling conditions of aluminum (AA6061) chips, in the presence of particulate alumina (Al2O3), to obtain a metal matrix composite of recycled aluminum (MMC-AlR) using hot press forging processes. The effects of temperature (430–530 °C) and holding time (60–120 min) were investigated. The introduction of 2.0 wt. % of Al2O3 to the aluminum matrix was based on preliminary research and some pilot tests. This study employed the 2k factorial design of experiments that should satisfy the operating temperatures (T) of 430 °C and 530 °C with holding times (t) of 60 min and 120 min. The central composite design (CCD) was utilized for RSM with the axial and center point to evaluate the responses to the ultimate tensile strength (UTS), elongation to failure (ETF), and microhardness (MH). Based on RSM, with the desirability of 97.6%, the significant parameters T = 530 °C and t = 120 min were suggested to yield an optimized composite performance with UTS = 317.99 MPa, ETF = 20.45%, and MH = 86.656 HV. Three confirmation runs were performed based on the suggested optimum parameters, and the error revealed was less than 25%. The mathematical models suggested by RSM could adequately describe the MMC-AlR responses of the factors being investigated.</jats:p>

Metabolite Profiling of Microwave-Assisted Sargassum fusiforme Extracts with Improved Antioxidant Activity Using Hybrid Response Surface Methodology and Artificial Neural Networking-Genetic Algorithm

“…Various other tests, including the coefficient of variation (CV), adjusted coefficient of determination (Adj. R 2 ), lack of fit, and coefficient of determination (R 2 ), were employed to validate the RSM model [ 23 ]. where y represents the predicted variables; β 0 , β i , β ii , and β ij represent the regression coefficients of the intercept coefficient, linear, quadratic, and second-order terms, respectively; X i and X j are the independent variables ( j and I range from 1 –z ), z is the number of factors ( z = 4); ε j is the error.…”

Effect of Thermally-Treated Chips on Density of AlMgSi Alloys Recycled Using Solid-State Technique

“…Besides the impacts on density, it was shown that the increment of the alumina in the aluminium matrix also affected mechanical properties of the extrudate due to dispersion hardening. As found by Chiba et al [14], Wagiman et al [30] and Azlan et al [31], the extrudate made of recycling chip exhibited higher strength properties compared to the as-received material.…”

Optimization of Tensile Strength and Young’s Modulus of CNT–CF/Epoxy Composites Using Response Surface Methodology (RSM)

“…From Table 9 , it can be seen that the determination coefficients, R 2 , were relatively high, between 0.7 and 0.9. Generally, a good model fit has an R 2 value of at least 0.8 [ 34 ]. When the R 2 value is closer to unity (1), the model’s agreement with the experimental data is better, with a lesser difference between the calculated and measured values [ 35 ].…”

“…Therefore, when the AP value is greater than four, the model discrimination is considered satisfactory [ 38 ]. Moreover, the predicted values’ ranges at the design point to the average prediction error are compared using AP [ 34 ]. Therefore, the AP obtained from the experimental values could be considered adequate, as most values were around four, which is a good sign and implies that the model can be used to navigate the design space [ 37 ].…”